Ethics of Artificial Intelligence

AI may involve any number of computational techniques to

achieve these aims, be that classical symbol-manipulating AI, inspired

by natural cognition, or machine learning via neural networks

(Goodfellow, Bengio, and Courville 2016; Silver et al. 2018). Furthermore, a power analysis could be complemented by insights from the tradition of critical theory. Critical theory is not a full-fledged normative theory that explains what is right and what is wrong in the way that classic theories like consequentialism, deontology, or virtue ethics do, but it does take in a normative stance. Just like critical theory, AI ethics is not meant to be an ethical theory in the classic sense, but it should diagnose technological advancements in society and change them for the better. As others have argued before (Delanty & Harris, 2021; Feenberg, 1991; Fuchs, 2016), critical theory offers a valuable toolbox for analyzing the societal implications of modern technologies.

As indicated in the previous sections, the practice of using AI systems is poor in terms of compliance with the principles set out in the various ethical guidelines. For example, many privacy-friendly techniques for the use of data sets and learning algorithms have been developed, using methods where AI systems’ “sight” is “darkened” via cryptography, differential or stochastic privacy (Ekstrand et al. 2018; Baron and Musolesi 2017; Duchi et al. 2013; Singla et al. 2014). Nevertheless, this contradicts the observation that AI has been making such massive progress for several years precisely because of the large amounts of (personal) data available. Those data are collected by privacy-invasive social media platforms, smartphone apps, as well as Internet of Things devices with its countless sensors.

WHO guidance on Artificial Intelligence to improve healthcare, mitigate risks worldwide

Second, what it means for AI to be ethical is not well understood; and once understood, it is likely to be the case that there are different ethical foundations that are not compatible with each other. Third, for international conflict and for conflict with nonstate actors, terror groups and crime groups – there will be AI on both sides. But companies must be intentional about designing and following ethical AI policies, so they improve the lives of consumers with AI-based products while avoiding unnecessary risks. Respecting the ethics of AI production has wide-ranging upsides for society, but companies also have much to gain from observing ethical AI practices. AI ethics may require organizations to establish policies that respect data privacy laws, account for bias in algorithms and explain to customers how their data is used before they sign up for a product. Altman’s call at a May 2023 Senate hearing for government regulation of AI shows greater awareness of the problem.

However, principles alone may not be sufficient, and the challenge lies in bringing clarity to these ethical foundations. One of the major practical difficulties is to actually enforce

regulation, both on the level of the state and on the level of the

individual who has a claim. They must identify the responsible legal

entity, prove the action, perhaps prove intent, find a court that

declares itself competent … and eventually get the court to

actually enforce its decision.

AI Ethics: What It Is And Why It Matters

For each section within these themes, we provide a general explanation

of the ethical issues, outline existing positions

and arguments, then analyse how these play out with current

technologies and finally, what policy consequences

may be drawn. Countries, and even cities and counties, are taking their own approaches to AI Ethics. This implies that, as a business, one needs to be aware of AI regulations at the country and even city level.

Even in less extreme cases, AI can cause harm to individuals by making people feel more isolated or addicted to their devices. Relying on the addictiveness of an app to generate more profits raises questions around the intentions of a mobile game, and companies with an AI ethics policy may choose to change or discontinue the game altogether. In fields like healthcare, for example, businesses are charged with handling sensitive data and performing actions that can alter people’s lives. Following the ethics of AI is then crucial to protecting valuable information, perfecting vital processes and avoiding the reputational or legal damages that come with irresponsible decision-making. The companies we spoke to wanted instead to be viewed as responsible stewards of people’s data.

What is ‘ethical AI’ and how can companies achieve it?

Aguilar found the philosophical thought experiment the “Trolley Problem” applicable in his research. The trolley problem is a moral dilemma that questions whether it is morally acceptable to sacrifice one to save a greater number. In education, is a teacher a rule-follower (“deontological” perspective) or outcome-seeker (“consequentialist” perspective)? Educators would have to decide when, where and how students can use generative AI in the classroom. “What we found was that women teachers in our study were more likely to rate their deontological approaches higher,” said Aguilar. “Male teachers cared more about the consequences of AI.” Female teachers supported rule-based (deontological) perspectives when compared to male teachers.

Many organizations have come around to seeing the business imperative of an AI ethical risk program. Countless news reports — from faulty and discriminatory facial recognition to privacy violations to black box algorithms with life-altering consequences — have put it on the agendas of boards, CEOs, and Chief Data and Analytics Officers. What most leaders don’t understand, however, is that addressing these risks requires raising awareness of them across their entire organization. With transparency issues, confirmation bias, and the other risks that we’ve discussed here, it’s essential you set up an ethical AI framework for your organization while you’re still at the start of your AI journey. “What gives me the most hope is that most people, regardless of where they are from, want AI and technology in general to be used in more ethical ways.

3 Opacity of AI Systems

We help to democratize access to these powerful technologies, regardless of the company size. With no clear protocol in place companies end up overlooking risks or scrambling to solve issues as they come up. Alternatively, humans can be involved in the review process, if not the decision-making one. Now that you know why ethical AI for business is essential and the reasons behind setting limits of ethical AI, let’s discuss possible solutions for the most pressing issues. Confirmation bias is a serious cause for concern for algorithms that aren’t trained by a diverse background or are fed biased datasets.

So debates regarding the norms according to which we want AI to act, whether we should grant AI rights, and whether the technology poses an existential risk or not, all express a concern for the human’s position in relation to the (potential) power of AI. Irrespective of such considerations is ai ethical on the microsociological level, the relative ineffectiveness of ethics can also be explained at the macrosociological level. This strive for a profitable use of machine learning systems is not primarily framed by value- or principle-based ethics, but obviously by an economic logic.

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It has moved to shut down services, including its Podcasts app, and cut features from Google Assistant. The ad sales that fund its sprawling pursuits have grown less reliably in the post-pandemic economy and been trimmed by new regulations and court orders on privacy and anticompetitive behavior. In addition to the departure of its leader, Gennai, RESIN also saw one of its most influential members, Sara Tangdall, lead AI principles ethics specialist, leave this month. She is now responsible AI product director at Salesforce, according to her LinkedIn profile.